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Statistical Sensitivity for Detection of Spatial and Temporal Patterns in Rodent Population Densities

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Statistical Sensitivity for Detection of Spatial and Temporal Patterns in Rodent Population Densities Hantavirus Statistical Sensitivity for Detection of Spatial and Temporal Patterns in Rodent Population Densities Cheryl A. Parmenter, Terry L. Yates, Robert R. Parmenter, and Jonathan L. Dunnum University of New Mexico, Albuquerque, New Mexico, USA A long-term monitoring program begun 1 year after the epidemic of hantavirus pulmonary syndrome in the U.S. Southwest tracked rodent density changes through time and among sites and related these changes to hantavirus infection rates in various small-mammal reservoir species and human disease outbreaks. We assessed the statistical sensitivity of the program’s field design and tested for potential biases in population estimates due to unintended deaths of rodents. Analyzing data from two sites in New Mexico from 1994 to 1998, we found that for many species of Peromyscus, Reithrodontomys, Neotoma, Dipodomys, and Perognathus, the monitoring program detected species-specific spatial and temporal differences in rodent densities; trap- related deaths did not significantly affect long-term population estimates. The program also detected a short-term increase in rodent densities in the winter of 1997-98, demonstrating its usefulness in identifying conditions conducive to increased risk for human disease. We analyzed the statistical capabilities of the desert grasses (Sporobolus spp. and Bouteloua long-term rodent monitoring program begun in eriopoda). The second site (Placitas), a pinyon- 1994 to detect spatial and temporal changes in juniper woodland site located in Sandoval County rodent densities and determine if the low death in the foothills of the Sandia Mountains, Cibola rates at all study sites resulted in biased (under- National Forest, approximately 30 km north of estimated) rodent population density estimates. Albuquerque, New Mexico, USA (35º 16.7'N, 106º We also examined a short-term subset of the data 18.6'W, elevation 1,830 m), was dominated by (mid-1997 to early 1998) to test whether the pinyon pine (Pinus edulis), one-seed juniper, and program design could statistically detect a sudden blue grama grass (Bouteloua gracilis). rodent increase in density that may precede a hantavirus pulmonary syndrome outbreak. Statistical Methods The overall experimental design and field Study Sites sampling procedures are described by Mills et al. We selected two monitoring sites in New (this issue, pp. 95-101). The rodent density estimates Mexico for analysis because they provided the we analyzed were based on mark-recapture longest period of field sampling, the greatest trapping data from three permanently marked range in rodent species richness, and the largest trapping webs at each site (1,2). We used difference in habitat types. The first, a desert monthly trapping data collected from August grassland site 90 km south of Albuquerque, on 1994 to February 1998. Trapping and rodent the Sevilleta National Wildlife Refuge, Socorro handling methods were described by Parmenter County, New Mexico, USA (34º 21.3'N, 106º et al. (3), and safety procedures during animal 53.1'W, elevation 1,465 m), was dominated by one- processing followed Mills et al. (4). Blood samples seed juniper (Juniperus monosperma), honey collected from Peromyscus maniculatus were mesquite (Prosopis glandulosa), and various analyzed by the Centers for Disease Control and Prevention for Sin Nombre virus (SNV). Rodent Address for correspondence: Cheryl A. Parmenter, Depart- densities were calculated by the program ment of Biology, The University of New Mexico, 167 Castetter Hall, Albuquerque, NM 87131-1091, USA; fax: 505-277-0304; e- DISTANCE (2). Each dataset was analyzed by mail: [email protected]. three models (uniform, half-normal, and hazard) Emerging Infectious Diseases118 Vol. 5, No. 1, JanuaryFebruary 1999 Hantavirus with three possible model adjustments (cosine, ern Biology. These offspring were included for the polynomial, and hermite). Akaikes information duration of the expected lifespan on each study criterion was then used to select the model that site. Thus, if a pregnant female N. albigula died best fit the particular dataset (2). The three web during the study, we would add two offspring density estimates for each trapping period were (the mean litter size for this species in New then partitioned into proportional densities Mexico) to the MNA estimates for the full three representing each species, on the basis of the trapping periods of their life expectancy. This relative proportion of each in the total web process created species-specific hypothetical MNA sample. These species-specific densities (in values that were either equal to or greater than numbers of mice per hectare) were analyzed by a the observed MNA values. The two MNA datasets repeated measures analysis of variance were then compared by RMANOVA. (RMANOVA) to test for differences between sites and through time and for site and time Spatial Differences in Rodent Densities interactions. If significant F values were observed, RMANOVA successfully distinguished ro- we conducted within-trapping-period tests to dent densities between sites for a number of detect differences among site means; we used species (Table 1). Ords kangaroo rat and the Fishers least-significant-difference method. The effect of low death rates among rodents on estimates of population size was analyzed by Table 1. Repeated-measures analysis of variance testing for differences among representative rodent minimum number alive (MNA) methods (5). For population densitiesa, Sevilleta National Wildlife Refuge this analysis, we calculated the observed MNA and Placitas, 1994-1998 values for each species during each sampling Species Source DF F value p period on the basis of actual field data, which Dipodomys Site 1 7.52 0.0517 ordii Error (Site) 4 included occasional trap deaths of rodents. We (Ords kangaroo Time 35 2.19 0.0007 then constructed a hypothetical MNA, assuming rat) Time x Site 35 2.21 0.0006 that no trap deaths occurred in the sampled Error (Time) 140 populations. The hypothetical death-free MNAs Perognathus Site 1 25.69 0.0071 were computed by extending the projected life flavescens Error (Site) 4 span of each animal that died in the trap. The (Plains pocket Time 35 2.91 0.0001 mouse) Time x Site 35 2.89 0.0001 length of the extensions differed by species and Error (Time) 140 site and was determined by the mean number of Peromyscus Site 1 0.28 0.6233 trapping periods during which each species maniculatus Error (Site) 4 would normally have been present on each site (Deer mouse) Time 35 2.80 0.0001 (this figure was based on the lifespans of all other Time x Site 35 1.18 0.2513 mice of that species that did not die in the traps Error (Time) 140 or during handling). For example, if Neotoma Peromyscus Site 1 0.11 0.7609 leucopus Error (Site) 4 albigula at the Sevilleta National Wildlife Refuge (White-footed Time 35 7.25 0.0001 site, Web 1, had a mean lifespan of three mouse) Time x Site 35 4.56 0.0001 trapping periods (i.e., its initial capture period Error (Time) 140 [time zero] plus three additional sampling periods), Peromyscus truei Site 1 1.98 0.2328 and a mouse died in a trap on its first capture, we (Pinyon mouse) Error (Site) 4 would add one mouse to the observed MNA Time 35 3.77 0.0001 Time x Site 35 3.29 0.0001 values for three additional trapping periods to Error (Time) 140 arrive at the hypothetical MNA values. If the Neotoma Site 1 6.65 0.0614 mouse died during its second trapping period, we albigula Error (Site) 4 would add two trapping periods to the MNA (White-throated Time 35 1.53 0.0431 estimates, and so on. In addition, if the dead mouse wood rat) Time x Site 35 1.65 0.0221 was pregnant or lactating, we increased the Error (Time) 140 hypothetical MNA by the average number of Reithrodontomys Site 1 0.34 0.5906 offspring that would have been produced; mean megalotis Error (Site) 4 (Western harvest Time 35 4.04 0.0001 numbers of offspring for each species were mouse) Time x Site 35 3.57 0.0001 determined from specimen databases at the Error (Time) 140 University of New Mexicos Museum of Southwest- aDensity=number of mice per hectare. Vol. 5, No. 1, JanuaryFebruary 1999119 Emerging Infectious Diseases Hantavirus Plains pocket mouse (Heteromyidae) were much C more abundant at the Sevilleta National Wildlife Refuge site than at Placitas (Figure 1A, B) and had greater statistical differences in the RMANOVA results (Table 1). In contrast, other rodent species (Muridae) had no overall differences by site (Table 1) and usually had similar densities, except for occasional episodes (Figures 1C-G). Although we observed no overall effect of site on D density for these species, Fishers least- significant-difference methods showed significant differences between sites during certain periods (Figures 1C,D,F,G), demonstrating that within a particular species, intersite differences could be discerned in both long-term sequences and during episodic, site-specific population irruptions. Temporal Changes in Rodent Densities E The analyses also detected changes in rodent densities through time in all species examined (Table 1). Several species with generally low densities (e.g., the harvest mouse [Figure 1D], the white-footed mouse [Figure 1E], and the deer mouse [Figure 1G]) occasionally became locally extinct but periodically recolonized the sites. Other species (e.g., the pinyon mouse [Figure 1F] F and the white-throated wood rat [Figure 1C]) were found consistently on both sites, although their densities fluctuated considerably. In all cases, RMANOVA found significant differences in these temporal patterns. G A Figure 1. Mean densities of rodents at the Sevilleta B National Wildlife Refuge (solid circles) and Placitas (open circles) study sites. Asterisks indicate significantly different means between sites (Fishers least-significant-difference tests, p < 0.05).
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